Md. Misbah Khan

2papers

2 Papers

2.3ROMar 11
STM32-Based Smart Waste Bin for Hygienic Disposal Using Embedded Sensing and Automated Control

Mohammed Aman Bhuiyan, Aritra Islam Saswato, Md. Misbah Khan et al.

The increasing demand for hygienic and contactless solutions in public and private environments has encouraged the development of automated systems for everyday applications. This paper presents the design and implementation of a motion- sensing automatic waste bin using an STM32 microcontroller, ultrasonic sensors, and a servo motor. The system detects user presence through ultrasonic sensing and automatically opens the bin lid using a servo motor controlled by the microcontroller. An additional ultrasonic sensor is used to monitor the internal waste level of the bin, while an OLED display provides real- time feedback regarding system status. The proposed system offers a low-cost, reliable, and easily deployable solution for touch-free waste disposal. Experimental evaluation demonstrates fast response time, stable sensing performance, and smooth mechanical operation. The system can be effectively deployed in homes, educational institutions, hospitals, and public facilities to improve hygiene and user convenience.

CVNov 24, 2025
OncoVision: Integrating Mammography and Clinical Data through Attention-Driven Multimodal AI for Enhanced Breast Cancer Diagnosis

Istiak Ahmed, Galib Ahmed, K. Shahriar Sanjid et al.

OncoVision is a multimodal AI pipeline that combines mammography images and clinical data for better breast cancer diagnosis. Employing an attention-based encoder-decoder backbone, it jointly segments four ROIs - masses, calcifications, axillary findings, and breast tissues - with state-of-the-art accuracy and robustly predicts ten structured clinical features: mass morphology, calcification type, ACR breast density, and BI-RADS categories. To fuse imaging and clinical insights, we developed two late-fusion strategies. By utilizing complementary multimodal data, late fusion strategies improve diagnostic precision and reduce inter-observer variability. Operationalized as a secure, user-friendly web application, OncoVision produces structured reports with dual-confidence scoring and attention-weighted visualizations for real-time diagnostic support to improve clinician trust and facilitate medical teaching. It can be easily incorporated into the clinic, making screening available in underprivileged areas around the world, such as rural South Asia. Combining accurate segmentation with clinical intuition, OncoVision raises the bar for AI-based mammography, offering a scalable and equitable solution to detect breast cancer at an earlier stage and enhancing treatment through timely interventions.